Summary of Examining the Commitments and Difficulties Inherent in Multimodal Foundation Models For Street View Imagery, by Zhenyuan Yang et al.
Examining the Commitments and Difficulties Inherent in Multimodal Foundation Models for Street View Imagery
by Zhenyuan Yang, Xuhui Lin, Qinyi He, Ziye Huang, Zhengliang Liu, Hanqi Jiang, Peng Shu, Zihao Wu, Yiwei Li, Stephen Law, Gengchen Mai, Tianming Liu, Tao Yang
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the capabilities of ChatGPT-4V and Gemini Pro for Street View Imagery, Built Environment, and Interior by evaluating their performance across various tasks. The assessments include street furniture identification, pedestrian and car counts, and road width measurement in Street View Imagery; building function classification, building age analysis, building height analysis, and building structure classification in the Built Environment; and interior room classification, interior design style analysis, interior furniture counts, and interior length measurement in Interior. The results reveal proficiency in length measurement, style analysis, question answering, and basic image understanding, but highlight limitations in detailed recognition and counting tasks. While zero-shot learning shows potential, performance varies depending on the problem domains and image complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models and foundation models can do things that combine vision and language. It uses two specific models to test their abilities across different areas like identifying street furniture or recognizing building structures. The results show that these models are good at some tasks, like measuring lengths or analyzing designs, but struggle with more detailed recognition or counting tasks. Even when they’re not perfect, the models can still learn new things without needing extra training data. |
Keywords
» Artificial intelligence » Classification » Gemini » Question answering » Zero shot
